Steganographic image processing

ABSTRACT

Multi-bit auxiliary information is hidden in imagery (e.g., digital photographs, video), using steganographic techniques. Such information can be used for various purposes, including identifying an image as originating from a particular source. In some embodiments, certain bits of the auxiliary information effect no change to the image. A variety of different arrangements are disclosed.

RELATED APPLICATION DATA

[0001] This application claims priority to copending application Ser.No. 09/520,406, filed Mar. 8, 2000, which is a division of applicationSer. No. 09/338,995, filed Jun. 24, 1999, which is a continuation ofapplication Ser. No. 08/951,858, filed Oct. 16, 1997 (now U.S. Pat. No.6,026,193), which is a continuation of application Ser. No. 08/436,134,filed May 8, 1995 (now U.S. Pat. No. 5,748,763), which is acontinuation-in-part of three applications: Ser. No. 08/327,426, filedOct. 21, 1994 (now U.S. Pat. No. 5,768,426), Ser. No. 08/215,289, filedMar. 17, 1994 (abandoned), and Ser. No. 08/154,866, filed Nov. 18, 1993(abandoned). The present specification is essentially identical to thatof application Ser. No. 08/327,426, and comprises less subject matterthan that of the immediate parent, application Ser. No. 09/520,406.

FIELD OF THE INVENTION

[0002] The present invention relates to the embedding of robustidentification codes in electronic, optical and physical media, and thesubsequent, objective discernment of such codes for identificationpurposes even after intervening distortion or corruption of the media.

[0003] The invention is illustrated with reference to several exemplaryapplications, including identification/authentication coding ofelectronic imagery, serial data signals (e.g. audio and video), emulsionfilm, and paper currency, but is not so limited.

BACKGROUND AND SUMMARY OF THE INVENTION

[0004] “I would never put it in the power of any printer or publisher tosuppress or alter a work of mine, by making him master of the copy”

[0005] Thomas Paine, Rights of man, 1792.

[0006] “The printer dares not go beyond his licensed copy”

[0007] Milton, Aeropagetica, 1644.

[0008] Since time immemorial, unauthorized use and outright piracy ofproprietary source material has been a source of lost revenue,confusion, and artistic corruption.

[0009] These historical problems have been compounded by the advent ofdigital technology. With it, the technology of copying materials andredistributing them in unauthorized manners has reached new heights ofsophistication, and more importantly, omnipresence. Lacking objectivemeans for comparing an alleged copy of material with the original,owners and possible litigation proceedings are left with a subjectiveopinion of whether the alleged copy is stolen, or has been used in anunauthorized manner. Furthermore, there is no simple means of tracing apath to an original purchaser of the material, something which can bevaluable in tracing where a possible “leak” of the material firstoccurred.

[0010] A variety of methods for protecting commercial material have beenattempted. One is to scramble signals via an encoding method prior todistribution, and descramble prior to use. This technique, however,requires that both the original and later descrambled signals neverleave closed and controlled networks, lest they be intercepted andrecorded. Furthermore, this arrangement is of little use in the broadfield of mass marketing audio and visual material, where even a fewdollars extra cost causes a major reduction in market, and where thesignal must eventually be descrambled to be perceived, and thus can beeasily recorded.

[0011] Another class of techniques relies on modification of sourceaudio or video signals to include a subliminal identification signal,which can be sensed by electronic means. Examples of such systems arefound in U.S. Pat. No. 4,972,471 and European patent publication EP441,702, as well as * in Komatsu et al, “Authentication System UsingConcealed Image in Telematics,” Memoirs of the School of Science &Engineering, Waseda University, No. 52, p. 45-60 (1988) (Komatsu usesthe term “digital watermark” for this technique). An elementaryintroduction to these methods is found in the article “DigitalSignatures,” Byte Magazine, November, 1993, p. 309. These techniqueshave the common characteristic that deterministic signals with welldefined patterns and sequences within the source material convey theidentification information. For certain applications this is not adrawback. But in general, this is an inefficient form of embeddingidentification information for a variety of reasons: (a) the whole ofthe source material is not used; (b) deterministic patterns have ahigher likelihood of being discovered and removed by a would-be pirate;and (c) the signals are not generally ‘holographic’ in thatidentifications may be difficult to make given only sections of thewhole. (‘Holographic’ is used herein to refer to the property that theidentification information is distributed globally throughout the codedsignal, and can be fully discerned from an examination of even afraction of the coded signal. Coding of this type is sometimes termed“distributed” herein.) Among the cited references are descriptions ofseveral programs which perform steganography—described in one documentas “. . . the ancient art of hiding information in some otherwiseinconspicuous information.” These programs variously allow computerusers to hide their own messages inside digital image files and digitalaudio files. All do so by toggling the least significant bit (the lowestorder bit of a single data sample) of a given audio data stream orrasterized image. Some of these programs embed messages quite directlyinto the least significant bit, while other “pre-encrypt” or scramble amessage first and then embed the encrypted data into the leastsignificant bit.

[0012] Our current understanding of these programs is that theygenerally rely on error-free transmission of the of digital data inorder to correctly transmit a given message in its entirety. Typicallythe message is passed only once, i.e., it is not repeated. Theseprograms also seem to “take over” the least significant bit entirely,where actual data is obliterated and the message placed accordingly.This might mean that such codes could be easily erased by merelystripping off the least significant bit of all data values in a givenimage or audio file. It is these and other considerations which suggestthat the only similarity between our invention and the established artof steganography is in the placement of information into data files withminimal perceptibility. The specifics of embedding and the uses of thatburied information diverge from there.

[0013] Another cited reference is U.S. Pat. No. 5,325,167 to Melen. Inthe service of authenticating a given document, the high precisionscanning of that document reveals patterns and “microscopic grainstructure” which apparently is a kind of unique fingerprint for theunderlying document media, such as paper itself or post-appliedmaterials such as toner. Melen further teaches that scanning and storingthis fingerprint can later be used in authentication by scanning apurported document and comparing it to the original fingerprint.Applicant is aware of a similar idea employed in the very high precisionrecording of credit card magnetic strips, as reported in the Wall StreetJournal but which cannot presently be located, wherein very finemagnetic fluxuations tend to be unique from one card to the next, sothat credit card authentication could be achieved through pre-recordingthese fluxuations later to be compared to the recordings of thepurportedly same credit card.

[0014] Both of the foregoing techniques appear to rest on the sameidentification principles on which the mature science of fingerprintanalysis rests: the innate uniqueness of some localized physicalproperty. These methods then rely upon a single judgement and/ormeasurement of “similarity” or “correlation” between a suspect and apre-recording master. Though fingerprint analysis has brought this to ahigh art, these methods are nevertheless open to a claim thatpreparations of the samples, and the “filtering” and “scannerspecifications” of Melen's patent, unavoidably tend to bias theresulting judgement of similarity, and would create a need for moreesoteric “expert testimony” to explain the confidence of a found matchor mis-match. An object of the present invention is to avoid thisreliance on expert testimony and to place the confidence in a match intosimple “coin flip” vernacular, i.e., what are the odds you can call thecorrect coin flip 16 times in a row. Attempts to identify fragments of afingerprint, document, or otherwise, exacerbate this issue of confidencein a judgment, where it is an object of the present invention toobjectively apply the intuitive “coin flip” confidence to the smallestfragment possible. Also, storing unique fingerprints for each and everydocument or credit card magnetic strip, and having these fingerprintsreadily available for later cross-checking, should prove to be quite aneconomic undertaking. It is an object of this invention to allow for the“re-use” of noise codes and “snowy images” in the service of easingstorage requirements.

[0015] U.S. Pat. No. 4,921,278 to Shiang et al. teaches a kind ofspatial encryption technique wherein a signature or photograph issplayed out into what the untrained eye would refer to as noise, butwhich is actually a well defined structure referred to as Moirepatterns. The similarities of the present invention to Shiang's systemappear to be use of noise-like patterns which nevertheless carryinformation, and the use of this principle on credit cards and otheridentification cards.

[0016] Others of the cited patents deal with other techniques foridentification and/or authentication of signals or media. U.S. Pat. No.4,944,036 to Hyatt does not appear to be applicable to the presentinvention, but does point out that the term “signature” can be equallyapplied to signals which carry unique characteristics based on physicalstructure.

[0017] Despite the foregoing and other diverse work in the field ofidentification/authentication, there still remains a need for a reliableand efficient method for performing a positive identification between acopy of an original signal and the original. Desirably, this methodshould not only perform identification, it should also be able to conveysource-version information in order to better pinpoint the point ofsale. The method should not compromise the innate quality of materialwhich is being sold, as does the placement of localized logos on images.The method should be robust so that an identification can be made evenafter multiple copies have been made and/or compression anddecompression of the signal has taken place. The identification methodshould be largely uneraseable or “uncrackable.” The method should becapable of working even on fractional pieces of the original signal,such as a 10 second “riff” of an audio signal or the “clipped andpasted” sub-section of an original image.

[0018] The existence of such a method would have profound consequenceson piracy in that it could (a) cost effectively monitor for unauthorizeduses of material and perform “quick checks”; (b) become a deterrent tounauthorized uses when the method is known to be in use and theconsequences well publicized; and (c) provide unequivocal proof ofidentity, similar to fingerprint identification, in litigation, withpotentially more reliability than that of fingerprinting.

[0019] In accordance with an exemplary embodiment of the invention, theforegoing and additional objects are achieved by embedding animperceptible identification code throughout a source signal. In thepreferred embodiment, this embedding is achieved by modulating thesource signal with a small noise signal in a coded fashion. Moreparticularly, bits of a binary identification code are referenced, oneat a time, to control modulation of the source signal with the noisesignal.

[0020] The copy with the embedded signal (the “encoded” copy) becomesthe material which is sold, while the original is secured in a safeplace. The new copy is nearly identical to the original except under thefinest of scrutiny; thus, its commercial value is not compromised. Afterthe new copy has been sold and distributed and potentially distorted bymultiple copies, the present disclosure details methods for positivelyidentifying any suspect signal against the original.

[0021] Among its other advantages, the preferred embodiments' use ofidentification signals which are global (holographic) and which mimicnatural noise sources allows the maximization of identification signalenergy, as opposed to merely having it present ‘somewhere in theoriginal material.’ This allows the identification coding to be muchmore robust in the face of thousands of real world degradation processesand material transformations, such as cutting and cropping of imagery.

[0022] The foregoing and additional features and advantages of thepresent invention will be more readily apparent from the followingdetailed description thereof, which proceeds with reference to theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

[0023]FIG. 1 is a simple and classic depiction of a one dimensionaldigital signal which is discretized in both axes.

[0024]FIG. 2 is a general overview, with detailed description of steps,of the process of embedding an “imperceptible” identification signalonto another signal.

[0025]FIG. 3 is a step-wise description of how a suspected copy of anoriginal is identified.

[0026]FIG. 4 is a schematic view of an apparatus for pre-exposing filmwith identification information in accordance with another embodiment ofthe present invention.

[0027]FIG. 5 is a diagram of a “black box” embodiment of the presentinvention.

[0028]FIG. 6 is a schematic block diagram of the embodiment of FIG. 5.

[0029]FIG. 7 shows a variant of the FIG. 6 embodiment adapted to encodesuccessive sets of input data with different code words but with thesame noise data.

[0030]FIG. 8 shows a variant of the FIG. 6 embodiment adapted to encodeeach frame of a videotaped production with a unique code number.

[0031] FIGS. 9A-9C are representations of an industry standard noisesecond that can be used in one embodiment of the present invention.

[0032]FIG. 10 shows an integrated circuit used in detecting standardnoise codes.

[0033]FIG. 11 shows a process flow for detecting a standard noise codethat can be used in the FIG. 10 embodiment.

[0034]FIG. 12 is an embodiment employing a plurality of detectors inaccordance with another embodiment of the present invention.

DETAILED DESCRIPTION

[0035] In the following discussion of an illustrative embodiment, thewords “signal” and “image” are used interchangeably to refer to bothone, two, and even beyond two dimensions of digital signal. Exampleswill routinely switch back and forth between a one dimensionalaudio-type digital signal and a two dimensional image-type digitalsignal.

[0036] In order to fully describe the details of an illustrativeembodiment of the invention, it is necessary first to describe the basicproperties of a digital signal. FIG. 1 shows a classic representation ofa one dimensional digital signal. The x-axis defines the index numbersof sequence of digital “samples,” and the y-axis is the instantaneousvalue of the signal at that sample, being constrained to exist only at afinite number of levels defined as the “binary depth” of a digitalsample. The example depicted in FIG. 1 has the value of 2 to the fourthpower, or “4 bits,” giving 16 allowed states of the sample value.

[0037] For audio information such as sound waves, it is commonlyaccepted that the digitization process discretizes a continuousphenomena both in the time domain and in the signal level domain. Assuch, the process of digitization itself introduces a fundamental errorsource, in that it cannot record detail smaller than the discretizationinterval in either domain. The industry has referred to this, amongother ways, as “aliasing” in the time domain, and “quantization noise”in the signal level domain. Thus, there will always be a basic errorfloor of a digital signal. Pure quantization noise, measured in a rootmean square sense, is theoretically known to have the value of one overthe square root of twelve, or about 0.29 DN, where DN stands for‘Digital Number’ or the finest unit increment of the signal level. Forexample, a perfect 12-bit digitizer will have 4096 allowed DN with aninnate root mean square noise floor of 0.29 DN.

[0038] All known physical measurement processes add additional noise tothe transformation of a continuous signal into the digital form. Thequantization noise typically adds in quadrature (square root of the meansquares) to the “analog noise” of the measurement process, as it issometimes referred to.

[0039] With almost all commercial and technical processes, the use ofthe decibel scale is used as a measure of signal and noise in a givenrecording medium. The expression “signal-to-noise ratio” is generallyused, as it will be in this disclosure. As an example, this disclosurerefers to signal to noise ratios in terms of signal power and noisepower, thus 20 dB represents a 10 times increase in signal amplitude.

[0040] In summary, the presently preferred embodiments of the inventionembed an N-bit value onto an entire signal through the addition of avery low amplitude encodation signal which has the look of pure noise. Nis usually at least 8 and is capped on the higher end by ultimatesignal-to-noise considerations and “bit error” in retrieving anddecoding the N-bit value. As a practical matter, N is chosen based onapplication specific considerations, such as the number of uniquedifferent “signatures” that are desired. To illustrate, if N=128, thenthe number of unique digital signatures is in excess of 10^ ^ A38 (2^ ^128). This number is believed to be more than adequate to both identifythe material with sufficient statistical certainty and to index exactsale and distribution information.

[0041] The amplitude or power of this added signal is determined by theaesthetic and informational considerations of each and every applicationusing the present methodology. For instance, non-professional video canstand to have a higher embedded signal level without becoming noticeableto the average human eye, while high precision audio may only be able toaccept a relatively small signal level lest the human ear perceive anobjectionable increase in “hiss.” These statements are generalities andeach application has its own set of criteria in choosing the signallevel of the embedded identification signal. The higher the level ofembedded signal, the more corrupted a copy can be and still beidentified. On the other hand, the higher the level of embedded signal,the more objectionable the perceived noise might be, potentiallyimpacting the value of the distributed material.

[0042] To illustrate the range of different applications to which theprinciples of the present invention can be applied, the presentspecification details two different systems. The first (termed, for lackof a better name, a “batch encoding” system), applies identificationcoding to an existing data signal. The second (termed, for lack of abetter name, a “real time encoding” system), applies identificationcoding to a signal as it is produced. Those skilled in the art willrecognize that the principles of the present invention can be applied ina number of other contexts in addition to these particularly described.

[0043] The discussions of these two systems can be read in either order.Some readers may find the latter more intuitive than the former; forothers the contrary may be true.

[0044] Batch Encoding

[0045] The following discussion of a first class of embodiments is bestprefaced by a section defining relevant terms:

[0046] The original signal refers to either the original digital signalor the high quality digitized copy of a non-digital original.

[0047] The N-bit identification word refers to a unique identificationbinary value, typically having N range anywhere from 8 to 128, which isthe identification code ultimately placed onto the original signal viathe disclosed transformation process. In the illustrated embodiment,each N-bit identification word begins with the sequence of values‘0101,’ which is used to determine an optimization of thesignal-to-noise ratio in the identification procedure of a suspectsignal (see definition below).

[0048] The m'th bit value of the N-bit identification word is either azero or one corresponding to the value of the m'th place, reading leftto right, of the N-bit word. E.g., the first (m=1) bit value of the N=8identification word 01110100 is the value ‘0;’ the second bit value ofthis identification word is ‘1’, etc.

[0049] The m'th individual embedded code signal refers to a signal whichhas dimensions and extent precisely equal to the original signal (e.g.both are a 512 by 512 digital image), and which is (in the illustratedembodiment) an independent pseudo-random sequence of digital values.“Pseudo” pays homage to the difficulty in philosophically defining purerandomness, and also indicates that there are various acceptable ways ofgenerating the “random” signal. There will be exactly N individualembedded code signals associated with any given original signal.

[0050] The acceptable perceived noise level refers to anapplication-specific determination of how much “extra noise,” i.e.amplitude of the composite embedded code signal described next, can beadded to the original signal and still have an acceptable signal to sellor otherwise distribute. This disclosure uses a 1 dB increase in noiseas a typical value which might be acceptable, but this is quitearbitrary.

[0051] The composite embedded code signal refers to the signal which hasdimensions and extent precisely equal to the original signal, (e.g. bothare a 512 by 512 digital image), and which contains the addition andappropriate attenuation of the N individual embedded code signals. Theindividual embedded signals are generated on an arbitrary scale, whereasthe amplitude of the composite signal must not exceed the pre-setacceptable perceived noise level, hence the need for “attenuation” ofthe N added individual code signals.

[0052] The distributable signal refers to the nearly similar copy of theoriginal signal, consisting of the original signal plus the compositeembedded code signal. This is the signal which is distributed to theoutside community, having only slightly higher but acceptable “noiseproperties” than the original.

[0053] A suspect signal refers to a signal which has the generalappearance of the original and distributed signal and whose potentialidentification match to the original is being questioned. The suspectsignal is then analyzed to see if it matches the N-bit identificationword.

[0054] The detailed methodology of this first embodiment begins bystating that the N-bit identification word is encoded onto the originalsignal by having each of the m bit values multiply their correspondingindividual embedded code signals, the resultant being accumulated in thecomposite signal, the fully summed composite signal then beingattenuated down to the acceptable perceived noise amplitude, and theresultant composite signal added to the original to become thedistributable signal.

[0055] The original signal, the N-bit identification word, and all Nindividual embedded code signals are then stored away in a securedplace. A suspect signal is then found. This signal may have undergonemultiple copies, compressions and decompressions, resamplings ontodifferent spaced digital signals, transfers from digital to analog backto digital media, or any combination of these items. IF the signal stillappears similar to the original, i.e. its innate quality is notthoroughly destroyed by all of these transformations and noiseadditions, then depending on the signal to noise properties of theembedded signal, the identification process should function to someobjective degree of statistical confidence. The extent of corruption ofthe suspect signal and the original acceptable perceived noise level aretwo key parameters in determining an expected confidence level ofidentification.

[0056] The identification process on the suspected signal begins byresampling and aligning the suspected signal onto the digital format andextent of the original signal. Thus, if an image has been reduced by afactor of two, it needs to be digitally enlarged by that same factor.Likewise, if a piece of music has been “cut out,” but may still have thesame sampling rate as the original, it is necessary to register thiscut-out piece to the original, typically done by performing a localdigital cross-correlation of the two signals (a common digitaloperation), finding at what delay value the correlation peaks, thenusing this found delay value to register the cut piece to a segment ofthe original.

[0057] Once the suspect signal has been sample-spacing matched andregistered to the original, the signal levels of the suspect signalshould be matched in an rms sense to the signal level of the original.This can be done via a search on the parameters of offset,amplification, and gamma being optimized by using the minimum of themean squared error between the two signals as a function of the threeparameters. We can call the suspect signal normalized and registered atthis point, or just normalized for convenience.

[0058] The newly matched pair then has the original signal subtractedfrom the normalized suspect signal to produce a difference signal. Thedifference signal is then cross-correlated with each of the N individualembedded code signals and the peak cross-correlation value recorded. Thefirst four bit code (‘0101’) is used as a calibrator both on the meanvalues of the zero value and the one value, and on further registrationof the two signals if a finer signal to noise ratio is desired (i.e.,the optimal separation of the 0101 signal will indicate an optimalregistration of the two signals and will also indicate the probableexistence of the N-bit identification signal being present.) Theresulting peak cross-correlation values will form a noisy series offloating point numbers which can be transformed into 0's and 1's bytheir proximity to the mean values of 0 and 1 found by the 0101calibration sequence. If the suspect signal has indeed been derived fromthe original, the identification number resulting from the above processwill match the N-bit identification word of the original, bearing inmind either predicted or unknown “bit error” statistics. Signal-to-noiseconsiderations will determine if there will be some kind of “bit error”in the identification process, leading to a form of X% probability ofidentification where X might be desired to be 99.9% or whatever. If thesuspect copy is indeed not a copy of the original, an essentially randomsequence of 0's and 1's will be produced, as well as an apparent lack ofseparation of the resultant values. This is to say, if the resultantvalues are plotted on a histogram, the existence of the N-bitidentification signal will exhibit strong bi-level characteristics,whereas the non-existence of the code, or the existence of a differentcode of a different original, will exhibit a type of randomgaussian-like distribution. This histogram separation alone should besufficient for an identification, but it is even stronger proof ofidentification when an exact binary sequence can be objectivelyreproduced.

[0059] Specific Example

[0060] Imagine that we have taken a valuable picture of two heads ofstate at a cocktail party, pictures which are sure to earn somereasonable fee in the commercial market. We desire to sell this pictureand ensure that it is not used in an unauthorized or uncompensatedmanner. This and the following steps are summarized in FIG. 2.

[0061] Assume the picture is transformed into a positive color print. Wefirst scan this into a digitized form via a normal high quality blackand white scanner with a typical photometric spectral response curve.(It is possible to get better ultimate signal to noise ratios byscanning in each of the three primary colors of the color image, butthis nuance is not central to describing the basic process.)

[0062] Let us assume that the scanned image now becomes a 4000 by 4000pixel monochrome digital image with a grey scale accuracy defined by12-bit grey values or 4096 allowed levels. We will call this the“original digital image” realizing that this is the same as our“original signal” in the above definitions.

[0063] During the scanning process we have arbitrarily set absoluteblack to correspond to digital value ‘30’. We estimate that there is abasic 2 Digital Number root mean square noise existing on the originaldigital image, plus a theoretical noise (known in the industry as “shotnoise”) of the square root of the brightness value of any given pixel.In formula, we have:

<RMS Noise_(n,m) >=sqrt(4+(V _(n,m)−30))  (1)

[0064] Here, n and m are simple indexing values on rows and columns ofthe image ranging from 0 to 3999. Sqrt is the square root. V is the DNof a given indexed pixel on the original digital image. The < > bracketsaround the RMS noise merely indicates that this is an expected averagevalue, where it is clear that each and every pixel will have a randomerror individually. Thus, for a pixel value having 1200 as a digitalnumber or “brightness value”, we find that its expected rms noise valueis sqrt(1204)=34.70, which is quite close to 34.64, the square root of1200.

[0065] We furthermore realize that the square root of the innatebrightness value of a pixel is not precisely what the eye perceives as aminimum objectionable noise, thus we come up with the formula:

<RMS Addable Noise_(n,m) >=X*sqrt(4+(V _(n,m)−30)^ Y)  (2)

[0066] Where X and Y have been added as empirical parameters which wewill adjust, and “addable” noise refers to our acceptable perceivednoise level from the definitions above. We now intend to experiment withwhat exact value of X and Y we can choose, but we will do so at the sametime that we are performing the next steps in the process.

[0067] The next step in our process is to choose N of our N-bitidentification word. We decide that a 16 bit main identification valuewith its 65536 possible values will be sufficiently large to identifythe image as ours, and that we will be directly selling no more than 128copies of the image which we wish to track, giving 7 bits plus an eighthbit for an odd/even adding of the first 7 bits (i.e. an error checkingbit on the first seven). The total bits required now are at 4 bits forthe 0101 calibration sequence, 16 for the main identification, 8 for theversion, and we now throw in another 4 as a further error checking valueon the first 28 bits, giving 32 bits as N. The final 4 bits can use oneof many industry standard error checking methods to choose its fourvalues.

[0068] We now randomly determine the 16 bit main identification number,finding for example, 1101 0001 1001 1110; our first versions of theoriginal sold will have all 0's as the version identifier, and the errorchecking bits will fall out where they may. We now have our unique 32bit identification word which we will embed on the original digitalimage.

[0069] To do this, we generate 32 independent random 4000 by 4000encoding images for each bit of our 32 bit identification word. Themanner of generating these random images is revealing. There arenumerous ways to generate these. By far the simplest is to turn up thegain on the same scanner that was used to scan in the originalphotograph, only this time placing a pure black image as the input, thenscanning this 32 times. The only drawback to this technique is that itdoes require a large amount of memory and that “fixed pattern” noisewill be part of each independent “noise image.” But, the fixed patternnoise can be removed via normal “dark frame” subtraction techniques.Assume that we set the absolute black average value at digital number‘100,’ and that rather than finding a 2 DN rms noise as we did in thenormal gain setting, we now find an rms noise of 10 DN about each andevery pixel's mean value.

[0070] We next apply a mid-spatial-frequency bandpass filter (spatialconvolution) to each and every independent random image, essentiallyremoving the very high and the very low spatial frequencies from them.We remove the very low frequencies because simple real-world errorsources like geometrical warping, splotches on scanners,mis-registrations, and the like will exhibit themselves most at lowerfrequencies also, and so we want to concentrate our identificationsignal at higher spatial frequencies in order to avoid these types ofcorruptions. Likewise, we remove the higher frequencies because multiplegeneration copies of a given image, as well as compression-decompressiontransformations, tend to wipe out higher frequencies anyway, so there isno point in placing too much identification signal into thesefrequencies if they will be the ones most prone to being attenuated.Therefore, our new filtered independent noise images will be dominatedby mid-spatial frequencies. On a practical note, since we are using12-bit values on our scanner and we have removed the DC valueeffectively and our new rms noise will be slightly less than 10 digitalnumbers, it is useful to boil this down to a 6-bit value ranging from−32 through 0 to 31 as the resultant random image.

[0071] Next we add all of the random images together which have a ‘1’ intheir corresponding bit value of the 32-bit identification word,accumulating the result in a 16-bit signed integer image. This is theunattenuated and un-scaled version of the composite embedded signal.

[0072] Next we experiment visually with adding the composite embeddedsignal to the original digital image, through varying the X and Yparameters of equation 2. In formula, we visually iterate to bothmaximize X and to find the appropriate Y in the following:

V _(dist;n,m) =V _(orig;n,m) +V _(comp;n,m) *X*Sqrt(4+V _(orig;n,m) ^ Y)

[0073] where dist refers to the candidate distributable image, i.e. weare visually iterating to find what X and Y will give us an acceptableimage; orig refers to the pixel value of the original image; and comprefers to the pixel value of the composite image. The n's and m's stillindex rows and columns of the image and indicate that this operation isdone on all 4000 by 4000 pixels. The symbol V is the DN of a given pixeland a given image.

[0074] As an arbitrary assumption, now, we assume that our visualexperimentation has found that the value of X=0.025 and Y=0.6 areacceptable values when comparing the original image with the candidatedistributable image. This is to say, the distributable image with the“extra noise” is acceptably close to the original in an aesthetic sense.Note that since our individual random images had a random rms noisevalue around 10 DN, and that adding approximately 16 of these imagestogether will increase the composite noise to around 40 DN, the Xmultiplication value of 0.025 will bring the added rms noise back toaround 1 DN, or half the amplitude of our innate noise on the original.This is roughly a 1 dB gain in noise at the dark pixel values andcorrespondingly more at the brighter values modified by the Y value of0.6.

[0075] So with these two values of X and Y, we now have constructed ourfirst versions of a distributable copy of the original. Other versionswill merely create a new composite signal and possibly change the Xslightly if deemed necessary. We now lock up the original digital imagealong with the 32-bit identification word for each version, and the 32independent random 4-bit images, waiting for our first case of asuspected piracy of our original. Storage wise, this is about 14Megabytes for the original image and 32*0.5 bytes*16 million=256Megabytes for the random individual encoded images. This is quiteacceptable for a single valuable image. Some storage economy can begained by simple lossless compression.

[0076] Finding a Suspected Piracy of our Image

[0077] We sell our image and several months later find our two heads ofstate in the exact poses we sold them in, seemingly cut and lifted outof our image and placed into another stylized background scene. This new“suspect” image is being printed in 100,000 copies of a given magazineissue, let us say. We now go about determining if a portion of ouroriginal image has indeed been used in an unauthorized manner. FIG. 3summarizes the details.

[0078] The first step is to take an issue of the magazine, cut out thepage with the image on it, then carefully but not too carefully cut outthe two figures from the background image using ordinary scissors. Ifpossible, we will cut out only one connected piece rather than the twofigures separately. We paste this onto a black background and scan thisinto a digital form. Next we electronically flag or mask out the blackbackground, which is easy to do by visual inspection.

[0079] We now procure the original digital image from our secured placealong with the 32-bit identification word and the 32 individual embeddedimages. We place the original digital image onto our computer screenusing standard image manipulation software, and we roughly cut along thesame borders as our masked area of the suspect image, masking this imageat the same time in roughly the same manner. The word ‘roughly’ is usedsince an exact cutting is not needed, it merely aids the identificationstatistics to get it reasonably close.

[0080] Next we rescale the masked suspect image to roughly match thesize of our masked original digital image, that is, we digitally scaleup or down the suspect image and roughly overlay it on the originalimage. Once we have performed this rough registration, we then throw thetwo images into an automated scaling and registration program. Theprogram performs a search on the three parameters of x position, yposition, and spatial scale, with the figure of merit being the meansquared error between the two images given any given scale variable andx and y offset. This is a fairly standard image processing methodology.Typically this would be done using generally smooth interpolationtechniques and done to sub-pixel accuracy. The search method can be oneof many, where the simplex method is a typical one.

[0081] Once the optimal scaling and x-y position variables are found,next comes another search on optimizing the black level, brightnessgain, and gamma of the two images. Again, the figure of merit to be usedis mean squared error, and again the simplex or other searchmethodologies can be used to optimize the three variables. After thesethree variables are optimized, we apply their corrections to the suspectimage and align it to exactly the pixel spacing and masking of theoriginal digital image and its mask. We can now call this the standardmask.

[0082] The next step is to subtract the original digital image from thenewly normalized suspect image only within the standard mask region.This new image is called the difference image.

[0083] Then we step through all 32 individual random embedded images,doing a local cross-correlation between the masked difference image andthe masked individual embedded image. ‘Local’ refers to the idea thatone need only start correlating over an offset region of +/−1 pixels ofoffset between the nominal registration points of the two images foundduring the search procedures above. The peak correlation should be veryclose to the nominal registration point of 0,0 offset, and we can addthe 3 by 3 correlation values together to give one grand correlationvalue for each of the 32 individual bits of our 32-bit identificationword.

[0084] After doing this for all 32 bit places and their correspondingrandom images, we have a quasi-floating point sequence of 32 values. Thefirst four values represent our calibration signal of 0101. We now takethe mean of the first and third floating point value and call thisfloating point value ‘0,’ and we take the mean of the second and thefourth value and call this floating point value ‘1.’ We then stepthrough all remaining 28 bit values and assign either a ‘0’ or a ‘1’based simply on which mean value they are closer to. Stated simply, ifthe suspect image is indeed a copy of our original, the embedded 32-bitresulting code should match that of our records, and if it is not acopy, we should get general randomness. The third and the fourthpossibilities of 3) Is a copy but doesn't match identification numberand 4) isn't a copy but does match are, in the case of 3), possible ifthe signal to noise ratio of the process has plummeted, i.e. the‘suspect image’ is truly a very poor copy of the original, and in thecase of 4) is basically one chance in four billion since we were using a32-bit identification number. If we are truly worried about 4), we canjust have a second independent lab perform their own tests on adifferent issue of the same magazine. Finally, checking the error-checkbits against what the values give is one final and possibly overkillcheck on the whole process. In situations where signal to noise is apossible problem, these error checking bits might be eliminated withouttoo much harm.

[0085] Benefits

[0086] Now that a full description of the first embodiment has beendescribed via a detailed example, it is appropriate to point out therationale of some of the process steps and their benefits.

[0087] The ultimate benefits of the foregoing process are that obtainingan identification number is fully independent of the manners and methodsof preparing the difference image. That is to say, the manners ofpreparing the difference image, such as cutting, registering, scaling,etcetera, cannot increase the odds of finding an identification numberwhen none exists; it only helps the signal-to-noise ratio of theidentification process when a true identification number is present.Methods of preparing images for identification can be different fromeach other even, providing the possibility for multiple independentmethodologies for making a match.

[0088] The ability to obtain a match even on sub-sets of the originalsignal or image is a key point in today's information-rich world.Cutting and pasting both images and sound clips is becoming more common,allowing such an embodiment to be used in detecting a copy even whenoriginal material has been thus corrupted. Finally, the signal to noiseratio of matching should begin to become difficult only when the copymaterial itself has been significantly altered either by noise or bysignificant distortion; both of these also will affect that copy'scommercial value, so that trying to thwart the system can only be doneat the expense of a huge decrease in commercial value.

[0089] The fullest expression of the present system will come when itbecomes an industry standard and numerous independent groups set up withtheir own means or ‘in-house’ brand of applying embedded identificationnumbers and in their decipherment. Numerous independent groupidentification will further enhance the ultimate objectivity of themethod, thereby enhancing its appeal as an industry standard.

[0090] Use of True Polarity in Creating the Composite Embedded CodeSignal

[0091] The foregoing discussion made use of the 0 and 1 formalism ofbinary technology to accomplish its ends. Specifically, the 0's and 1'sof the N-bit identification word directly multiplied their correspondingindividual embedded code signal to form the composite embedded codesignal (step 8, FIG. 2). This approach certainly has its conceptualsimplicity, but the multiplication of an embedded code signal by 0 alongwith the storage of that embedded code contains a kind of inefficiency.

[0092] It is preferred to maintain the formalism of the 0 and 1 natureof the N-bit identification word, but to have the 0's of the word inducea subtraction of their corresponding embedded code signal. Thus, in step8 of FIG. 2, rather than only ‘adding’ the individual embedded codesignals which correspond to a ‘1’ in the N-bit identification word, wewill also ‘subtract’ the individual embedded code signals whichcorrespond to a ‘0’ in the N-bit identification word.

[0093] At first glance this seems to add more apparent noise to thefinal composite signal. But it also increases the energy-wise separationof the 0's from the 1's, and thus the ‘gain’ which is applied in step10, FIG. 2 can be correspondingly lower.

[0094] We can refer to this improvement as the use of true polarity. Themain advantage of this improvement can largely be summarized as‘informational efficiency.’

[0095] ‘Perceptual Orthogonality’ of the Individual Embedded CodeSignals

[0096] The foregoing discussion contemplates the use of generally randomnoise-like signals as the individual embedded code signals. This isperhaps the simplest form of signal to generate. However, there is aform of informational optimization which can be applied to the set ofthe individual embedded signals, which the applicant describes under therubric ‘perceptual orthogonality.’ This term is loosely based on themathematical concept of the orthogonality of vectors, with the currentadditional requirement that this orthogonality should maximize thesignal energy of the identification information while maintaining itbelow some perceptibility threshold. Put another way, the embedded codesignals need not necessarily be random in nature.

[0097] Use and Improvements of the First Embodiment in the Field ofEmulsion-Based Photographv

[0098] The foregoing discussion outlined techniques that are applicableto photographic materials. The following section explores the details ofthis area further and discloses certain improvements which lendthemselves to a broad range of applications.

[0099] The first area to be discussed involves the pre-application orpre-exposing of a serial number onto traditional photographic products,such as negative film, print paper, transparencies, etc. In general,this is a way to embed a priori unique serial numbers (and byimplication, ownership and tracking information) into photographicmaterial. The serial numbers themselves would be a permanent part of thenormally exposed picture, as opposed to being relegated to the marginsor stamped on the back of a printed photograph, which all requireseparate locations and separate methods of copying. The ‘serial number’as it is called here is generally synonymous with the N-bitidentification word, only now we are using a more common industrialterminology.

[0100] In FIG. 2, step 11, the disclosure calls for the storage of the“original [image]” along with code images. Then in FIG. 3, step 9, itdirects that the original be subtracted from the suspect image, therebyleaving the possible identification codes plus whatever noise andcorruption has accumulated. Therefore, the previous disclosure made thetacit assumption that there exists an original without the compositeembedded signals.

[0101] Now in the case of selling print paper and other duplication filmproducts, this will still be the case, i.e., an “original” without theembedded codes will indeed exist and the basic methodology of the firstembodiment can be employed. The original film serves perfectly well asan ‘unencoded original.’

[0102] However, in the case where pre-exposed negative film is used, thecomposite embedded signal pre-exists on the original film and thus therewill never be an “original” separate from the pre-embedded signal. It isthis latter case, therefore, which will be examined a bit more closely,along with observations on how to best use the principles discussedabove (the former cases adhering to the previously outlined methods).

[0103] The clearest point of departure for the case of pre-numberednegative film, i.e. negative film which has had each and every framepre-exposed with a very faint and unique composite embedded signal,comes at step 9 of FIG. 3 as previously noted. There are certainly otherdifferences as well, but they are mostly logistical in nature, such ashow and when to embed the signals on the film, how to store the codenumbers and serial number, etc. Obviously the pre-exposing of film wouldinvolve a major change to the general mass production process ofcreating and packaging film.

[0104]FIG. 4 has a schematic outlining one potential post-hoc mechanismfor pre-exposing film. ‘Post-hoc’ refers to applying a process after thefull common manufacturing process of film has already taken place.Eventually, economies of scale may dictate placing this pre-exposingprocess directly into the chain of manufacturing film. Depicted in FIG.4 is what is commonly known as a film writing system. The computer, 106,displays the composite signal produced in step 8, FIG. 2, on itsphosphor screen. A given frame of film is then exposed by imaging thisphosphor screen, where the exposure level is generally very faint, i.e.generally imperceptible. Clearly, the marketplace will set its owndemands on how faint this should be, that is, the level of added‘graininess’ as practitioners would put it. Each frame of film issequentially exposed, where in general the composite image displayed onthe CRT 102 is changed for each and every frame, thereby giving eachframe of film a different serial number. The transfer lens 104highlights the focal conjugate planes of a film frame and the CRT face.

[0105] Getting back to the applying the principles of the foregoingembodiment in the case of preexposed negative film . . . . At step 9,FIG. 3, if we were to subtract the “original” with its embedded code, wewould obviously be “erasing” the code as well since the code is anintegral part of the original. Fortunately, remedies do exist andidentifications can still be made. However, it will be a challenge toartisans who refine this embodiment to have the signal to noise ratio ofthe identification process in the pre-exposed negative case approach thesignal to noise ratio of the case where the un-encoded original exists.

[0106] A succinct definition of the problem is in order at this point.Given a suspect picture (signal), find the embedded identification codeIF a code exists at al. The problem reduces to one of finding theamplitude of each and every individual embedded code signal within thesuspect picture, not only within the context of noise and corruption aswas previously explained, but now also within the context of thecoupling between a captured image and the codes. ‘Coupling’ here refersto the idea that the captured image “randomly biases” thecross-correlation.

[0107] So, bearing in mind this additional item of signal coupling, theidentification process now estimates the signal amplitude of each andevery individual embedded code signal (as opposed to taking thecross-correlation result of step 12, FIG. 3). If our identificationsignal exists in the suspect picture, the amplitudes thus found willsplit into a polarity with positive amplitudes being assigned a ‘1’ andnegative amplitudes being assigned a ‘0’. Our unique identification codemanifests itself. If, on the other hand, no such identification codeexists or it is someone else's code, then a random gaussian-likedistribution of amplitudes is found with a random hash of values.

[0108] It remains to provide a few more details on how the amplitudes ofthe individual embedded codes are found. Again, fortunately, this exactproblem has been treated in other technological applications. Besides,throw this problem and a little food into a crowded room ofmathematicians and statisticians and surely a half dozen optimizedmethodologies will pop out after some reasonable period of time. It is arather cleanly defined problem.

[0109] One specific example solution comes from the field ofastronomical imaging. Here, it is a mature prior art to subtract out a“thermal noise frame” from a given CCD image of an object. Often,however, it is not precisely known what scaling factor to use insubtracting the thermal frame, and a search for the correct scalingfactor is performed. This is precisely the task of this step of thepresent embodiment.

[0110] General practice merely performs a common search algorithm on thescaling factor, where a scaling factor is chosen and a new image iscreated according to:

NEW IMAGE=ACQUIRED IMAGE−SCALE*THERMAL IMAGE

[0111] The new image is applied to the fast fourier transform routineand a scale factor is eventually found which minimizes the integratedhigh frequency content of the new image. This general type of searchoperation with its minimization of a particular quantity is exceedinglycommon. The scale factor thus found is the sought-for “amplitude.”Refinements which are contemplated but not yet implemented are where thecoupling of the higher derivatives of the acquired image and theembedded codes are estimated and removed from the calculated scalefactor. In other words, certain bias effects from the coupling mentionedearlier are present and should be eventually accounted for and removedboth through theoretical and empirical experimentation.

[0112] Use and Improvements in the Detection of Signal or ImageAlteration

[0113] Apart from the basic need of identifying a signal or image as awhole, there is also a rather ubiquitous need to detect possiblealterations to a signal or image. The following section describes howthe foregoing embodiment, with certain modifications and improvements,can be used as a powerful tool in this area. The potential scenarios andapplications of detecting alterations are innumerable.

[0114] To first summarize, assume that we have a given signal or imagewhich has been positively identified using the basic methods outlinedabove. In other words, we know its N-bit identification word, itsindividual embedded code signals, and its composite embedded code. Wecan then fairly simply create a spatial map of the composite code'samplitude within our given signal or image. Furthermore, we can dividethis amplitude map by the known composite code's spatial amplitude,giving a normalized map, i.e. a map which should fluctuate about someglobal mean value. By simple examination of this map, we can visuallydetect any areas which have been significantly altered wherein the valueof the normalized amplitude dips below some statistically set thresholdbased purely on typical noise and corruption (error).

[0115] The details of implementing the creation of the amplitude maphave a variety of choices. One is to perform the same procedure which isused to determine the signal amplitude as described above, only now westep and repeat the multiplication of any given area of the signal/imagewith a gaussian weight function centered about the area we areinvestigating.

[0116] Universal Versus Custom Codes

[0117] The disclosure thus far has outlined how each and every sourcesignal has its own unique set of individual embedded code signals. Thisentails the storage of a significant amount of additional codeinformation above and beyond the original, and many applications maymerit some form of economizing.

[0118] One such approach to economizing is to have a given set ofindividual embedded code signals be common to a batch of sourcematerials. For example, one thousand images can all utilize the samebasic set of individual embedded code signals. The storage requirementsof these codes then become a small fraction of the overall storagerequirements of the source material.

[0119] Furthermore, some applications can utilize a universal set ofindividual embedded code signals, i.e., codes which remain the same forall instances of distributed material. This type of requirement would beseen by systems which wish to hide the N-bit identification word itself,yet have standardized equipment be able to read that word. This can beused in systems which make go/no go decisions at point-of-readlocations. The potential drawback to this set-up is that the universalcodes are more prone to be sleuthed or stolen; therefore they will notbe as secure as the apparatus and methodology of the previouslydisclosed arrangement. Perhaps this is just the difference between ‘highsecurity’ and ‘air-tight security,’ a distinction carrying little weightwith the bulk of potential applications.

[0120] Use in Printing, Paper, Documents, Plastic Coated IdentificationCards, and Other Material Where Global Embedded Codes Can Be Imprinted

[0121] The term ‘signal’ is often used narrowly to refer to digital datainformation, audio signals, images, etc. A broader interpretation of‘signal,’ and the one more generally intended, includes any form ofmodulation of any material whatsoever. Thus, the micro-topology of apiece of common paper becomes a ‘signal’ (e.g. it height as a functionof x-y coordinates). The reflective properties of a flat piece ofplastic (as a function of space also) becomes a signal. The point isthat photographic emulsions, audio signals, and digitized informationare not the only types of signals capable of utilizing the principles ofthe present invention.

[0122] As a case in point, a machine very much resembling a brailleprinting machine can be designed so as to imprint unique ‘noise-like’indentations as outlined above. These indentations can be applied with apressure which is much smaller than is typically applied in creatingbraille, to the point where the patterns are not noticed by a normaluser of the paper. But by following the steps of the present disclosureand applying them via the mechanism of micro-indentations, a uniqueidentification code can be placed onto any given sheet of paper, be itintended for everyday stationary purposes, or be it for importantdocuments, legal tender, or other secured material.

[0123] The reading of the identification material in such an embodimentgenerally proceeds by merely reading the document optically at a varietyof angles. This would become an inexpensive method for deducing themicro-topology of the paper surface. Certainly other forms of readingthe topology of the paper are possible as well.

[0124] In the case of plastic encased material such as identificationcards, e.g. driver's licenses, a similar braille-like impressionsmachine can be utilized to imprint unique identification codes. Subtlelayers of photoreactive materials can also be embedded inside theplastic and ‘exposed.’

[0125] It is clear that wherever a material exists which is capable ofbeing modulated by ‘noise-like’ signals, that material is an appropriatecarrier for unique identification codes and utilization of theprinciples of the invention. All that remains is the matter ofeconomically applying the identification information and maintaining thesignal level below an acceptability threshold which each and everyapplication will define for itself.

[0126] Appendix A Description

[0127] Appendix A contains the source code of an implementation andverification of the foregoing embodiment for an 8-bit black and whiteimaging system.

[0128] Real Time Encoder

[0129] While the first class of embodiments most commonly employs astandard microprocessor or computer to perform the encodation of animage or signal, it is possible to utilize a custom encodation devicewhich may be faster than a typical Von Neuman-type processor. Such asystem can be utilized with all manner of serial data streams.

[0130] Music and videotape recordings are examples of serial datastreams—data streams which are often pirated. It would assistenforcement efforts if authorized recordings were encoded withidentification data so that pirated knock-offs could be traced to theoriginal from which they were made.

[0131] Piracy is but one concern driving the need for the presentinvention. Another is authentication. Often it is important to confirmthat a given set of data is really what it is purported to be (oftenseveral years after its generation).

[0132] To address these and other needs, the system 200 of FIG. 5 can beemployed. System 200 can be thought of as an identification coding blackbox 202. The system 200 receives an input signal (sometimes termed the“master” or “unencoded” signal) and a code word, and produces (generallyin real time) an identification-coded output signal. (Usually, thesystem provides key data for use in later decoding.) The contents of the“black box” 202 can take various forms. An exemplary black box system isshown in FIG. 6 and includes a look-up table 204, a digital noise source206, first and second scalers 208, 210, an adder/subtracter 212, amemory 214, and a register 216.

[0133] The input signal (which in the illustrated embodiment is an 8-20bit data signal provided at a rate of one million samples per second,but which in other embodiments could be an analog signal if appropriateA/D and D/A conversion is provided) is applied from an input 218 to theaddress input 220 of the look-up table 204. For each input sample (i.e.look-up table address), the table provides a corresponding 8-bit digitaloutput word. This output word is used as a scaling factor that isapplied to one input of the first scaler 208.

[0134] The first scaler 208 has a second input, to which is applied an8-bit digital noise signal from source 206. (In the illustratedembodiment, the noise source 206 comprises an analog noise source 222and an analog-to-digital converter 224 although, again, otherimplementations can be used.) The noise source in the illustratedembodiment has a zero mean output value, with a full width half maximum(FWHM) of 50-100 digital numbers (e.g. from −75 to +75).

[0135] The first scaler 208 multiplies the two 8-bit words at its inputs(scale factor and noise) to produce—for each sample of the system inputsignal—a 16-bit output word. Since the noise signal has a zero meanvalue, the output of the first scaler likewise has a zero mean value.

[0136] The output of the first scaler 208 is applied to the input of thesecond scaler 210. The second scaler serves a global scaling function,establishing the absolute magnitude of the identification signal thatwill ultimately be embedded into the input data signal. The scalingfactor is set through a scale control device 226 (which may take anumber of forms, from a simple rheostat to a graphically implementedcontrol in a graphical user interface), perrmitting this factor to bechanged in accordance with the requirements of different applications.The second scaler 210 provides on its output line 228 a scaled noisesignal. Each sample of this scaled noise signal is successively storedin the memory 214. (In the illustrated embodiment, the output from thefirst scaler 208 may range between -−1500 and +1500 (decimal), while theoutput from the second scaler 210 is in the low single digits, (such asbetween −2 and +2).)

[0137] Register 216 stores a multi-bit identification code word. In theillustrated embodiment this code word consists of 8 bits, althoughlarger code words (up to hundreds of bits) are commonly used. These bitsare referenced, one at a time, to control how the input signal ismodulated with the scaled noise signal.

[0138] In particular, a pointer 230 is cycled sequentially through thebit positions of the code word in register 216 to provide a control bitof “0” or “1” to a control input 232 of the adder/subtracter 212. If,for a particular input signal sample, the control bit is a “1”, thescaled noise signal sample on line 232 is added to the input signalsample. If the control bit is a “0”, the scaled noise signal sample issubtracted from the input signal sample. The output 234 from theadder/subtracter 212 provides the black box's output signal.

[0139] The addition or subtraction of the scaled noise signal inaccordance with the bits of the code word effects a modulation of theinput signal that is generally imperceptible. However, with knowledge ofthe contents of the memory 214, a user can later decode the encoding,determining the code number used in the original encoding process.(Actually, use of memory 214 is optional, as explained below.)

[0140] It will be recognized that the encoded signal can be distributedin well known ways, including converted to printed image form, stored onmagnetic media (floppy diskette, analog or DAT tape, etc.), CD-ROM, etc.etc.

[0141] Decoding

[0142] A variety of techniques can be used to determine theidentification code with which a suspect signal has been encoded. Twoare discussed below. The first is less preferable than the latter formost applications, but is discussed herein so that the reader may have afuller context within which to understand the invention.

[0143] More particularly, the first decoding method is a differencemethod, relying on subtraction of corresponding samples of the originalsignal from the suspect signal to obtain difference samples, which arethen examined (typically individually) for deterministic coding indicia(i.e. the stored noise data). This approach may thus be termed a“sample-based, deterministic” decoding technique.

[0144] The second decoding method does not make use of the originalsignal. Nor does it examine particular samples looking for predeterminednoise characteristics. Rather, the statistics of the suspect signal (ora portion thereof) are considered in the aggregate and analyzed todiscern the presence of identification coding that permeates the entiresignal. The reference to permeation means the entire identification codecan be discerned from a small fragment of the suspect signal. Thislatter approach may thus be termed a “holographic, statistical” decodingtechnique.

[0145] Both of these methods begin by registering the suspect signal tomatch the original. This entails scaling (e.g. in amplitude, duration,color balance, etc.), and sampling (or resampling) to restore theoriginal sample rate. As in the earlier described embodiment, there area variety of well understood techniques by which the operationsassociated with this registration function can be performed.

[0146] As noted, the first decoding approach proceeds by subtracting theoriginal signal from the registered, suspect signal, leaving adifference signal. The polarity of successive difference signal samplescan then be compared with the polarities of the corresponding storednoise signal samples to determine the identification code. That is, ifthe polarity of the first difference signal sample matches that of thefirst noise signal sample, then the first bit of the identification codeis a “1.” (In such case, the polarity of the 9th, 17th, 25th, etc.samples should also all be positive.) If the polarity of the firstdifference signal sample is opposite that of the corresponding noisesignal sample, then the first bit of the identification code is a “0.”

[0147] By conducting the foregoing analysis with eight successivesamples of the difference signal, the sequence of bits that comprise theoriginal code word can be determined. If, as in the preferredembodiment, pointer 230 stepped through the code word one bit at a time,beginning with the first bit, during encoding, then the first 8 samplesof the difference signal can be analyzed to uniquely determine the valueof the 8-bit code word.

[0148] In a noise-free world (speaking here of noise independent of thatwith which the identification coding is effected), the foregoinganalysis would always yield the correct identification code. But aprocess that is only applicable in a noise-free world is of limitedutility indeed.

[0149] (Further, accurate identification of signals in noise-freecontexts can be handled in a variety of other, simpler ways: e.g.checksums; statistically improbable correspondence between suspect andoriginal signals; etc.)

[0150] While noise-induced aberrations in decoding can be dealt with—tosome degree—by analyzing large portions of the signal, such aberrationsstill place a practical ceiling on the confidence of the process.Further, the villain that must be confronted is not always as benign asrandom noise. Rather, it increasingly takes the form of human-causedcorruption, distortion, manipulation, etc. In such cases, the desireddegree of identification confidence can only be achieved by otherapproaches.

[0151] The presently preferred approach (the “holographic, statistical”decoding technique) relies on recombining the suspect signal withcertain noise data (typically the data stored in memory 214), andanalyzing the entropy of the resulting signal. “Entropy” need not beunderstood in its most strict mathematical definition, it being merelythe most concise word to describe randomness (noise, smoothness,snowiness, etc.).

[0152] Most serial data signals are not random. That is, one sampleusually correlates—to some degree—with the adjacent samples. Noise, incontrast, typically is random. If a random signal (e.g. noise) is addedto (or subtracted from) a non-random signal, the entropy of theresulting signal generally increases. That is, the resulting signal hasmore random variations than the original signal. This is the case withthe encoded output signal produced by the present encoding process; ithas more entropy than the original, unencoded signal.

[0153] If, in contrast, the addition of a random signal to (orsubtraction from) a non-random signal reduces entropy, then somethingunusual is happening. It is this anomaly that the preferred decodingprocess uses to detect embedded identification coding.

[0154] To fully understand this entropy-based decoding method, it isfirst helpful to highlight a characteristic of the original encodingprocess: the similar treatment of every eighth sample.

[0155] In the encoding process discussed above, the pointer 230increments through the code word, one bit for each successive sample ofthe input signal. If the code word is eight bits in length, then thepointer returns to the same bit position in the code word every eighthsignal sample. If this bit is a “1”, noise is added to the input signal;if this bit is a “0”, noise is subtracted from the input signal. Due tothe cyclic progression of the pointer 230, every eighth sample of anencoded signal thus shares a characteristic: they are all eitheraugmented by the corresponding noise data (which may be negative), orthey are all diminished, depending on whether the bit of the code wordthen being addressed by pointer 230 is a “I” or a “0”.

[0156] To exploit this characteristic, the entropy-based decodingprocess treats every eighth sample of the suspect signal in likefashion. In particular, the process begins by adding to the 1st, 9th,17th, 25th, etc. samples of the suspect signal the corresponding scalednoise signal values stored in the memory 214 (i.e. those stored in the1st, 9th, 17th, 25th, etc., memory locations, respectively). The entropyof the resulting signal (i.e. the suspect signal with every 8th samplemodified) is then computed.

[0157] (Computation of a signal's entropy or randomness is wellunderstood by artisans in this field. One generally accepted techniqueis to take the derivative of the signal at each sample point, squarethese values, and then sum over the entire signal. However, a variety ofother well known techniques can alternatively be used.)

[0158] The foregoing step is then repeated, this time subtracting thestored noise values from the 1st, 9th, 17th, 25 etc. suspect signalsamples.

[0159] One of these two operations will undo the encoding process andreduce the resulting signal's entropy; the other will aggravate it. Ifadding the noise data in memory 214 to the suspect signal reduces itsentropy, then this data must earlier have been subtracted from theoriginal signal. This indicates that pointer 230 was pointing to a “0”bit when these samples were encoded. (A “0” at the control input ofadder/subtracter 212 caused it to subtract the scaled noise from theinput signal.)

[0160] Conversely, if subtracting the noise data from every eighthsample of the suspect signal reduces its entropy, then the encodingprocess must have earlier added this noise. This indicates that pointer230 was pointing to a “1” bit when samples 1, 9, 17, 25, etc., wereencoded.

[0161] By noting whether entropy decreases by (a) adding or (b)subtracting the stored noise data to/from the suspect signal, it can bedetermined that the first bit of the code word is (a) a “0”, or (b) a“1.”

[0162] The foregoing operations are then conducted for the group ofspaced samples of the suspect signal beginning with the second sample(i.e. 2, 10, 18, 26 . . . ). The entropy of the resulting signalsindicate whether the second bit of the code word is a “0” or a “1”.Likewise with the following 6 groups of spaced samples in the suspectsignal, until all 8 bits of the code word have been discerned.

[0163] It will be appreciated that the foregoing approach is notsensitive to corruption mechanisms that alter the values of individualsamples; instead, the process considers the entropy of the signal as awhole, yielding a high degree of confidence in the results. Further,even small excerpts of the signal can be analyzed in this manner,permitting piracy of even small details of an original work to bedetected. The results are thus statistically robust, both in the face ofnatural and human corruption of the suspect signal.

[0164] Illustrative Variations

[0165] From the foregoing description, it will be recognized thatnumerous modifications can be made to the illustrated systems withoutchanging the fundamental principles. A few of these variations aredescribed below.

[0166] The above-described decoding process tries both adding andsubtracting stored noise data to/from the suspect signal in order tofind which operation reduces entropy. In other embodiments, only one ofthese operations needs to be conducted. For example, in one alternativedecoding process the stored noise data corresponding to every eighthsample of the suspect signal is only added to said samples. If theentropy of the resulting signal is thereby increased, then thecorresponding bit of the code word is a “1” (i.e. this noise was addedearlier, during the encoding process, so adding it again only compoundsthe signal's randomness). If the entropy of the resulting signal isthereby decreased, then the corresponding bit of the code word is a “0”.A further test of entropy if the stored noise samples are subtracted isnot required.

[0167] The statistical reliability of the identification process (codingand decoding) can be designed to exceed virtually any confidencethreshold (e.g. 99.9%, 99.99%, 99.999%, etc. confidence) by appropriateselection of the global scaling factors, etc. Additional confidence inany given application (unnecessary in most applications) can be achievedby rechecking the decoding process.

[0168] One way to recheck the decoding process is to remove the storednoise data from the suspect signal in accordance with the bits of thediscerned code word, yielding a “restored” signal (e.g. if the first bitof the code word is found to be “1,” then the noise samples stored inthe 1st, 9th, 17th, etc. locations of the memory 214 are subtracted fromthe corresponding samples of the suspect signal). The entropy of therestored signal is measured and used as a baseline in furthermeasurements. Next, the process is repeated, this time removing thestored noise data from the suspect signal in accordance with a modifiedcode word. The modified code word is the same as the discerned codeword, except 1 bit is toggled (e.g. the first). The entropy of theresulting signal is determined, and compared with the baseline. If thetoggling of the bit in the discerned code word resulted in increasedentropy, then the accuracy of that bit of the discerned code word isconfirmed. The process repeats, each time with a different bit of thediscerned code word toggled, until all bits of the code word have beenso checked. Each change should result in an increase in entropy comparedto the baseline value.

[0169] The data stored in memory 214 is subject to a variety ofalternatives. In the foregoing discussion, memory 214 contains thescaled noise data. In other embodiments, the unscaled noise data can bestored instead.

[0170] In still other embodiments, it can be desirable to store at leastpart of the input signal itself in memory 214. For example, the memorycan allocate 8 signed bits to the noise sample, and 16 bits to store themost significant bits of an 18- or 20-bit audio signal sample. This hasseveral benefits. One is that it simplifies registration of a “suspect”signal. Another is that, in the case of encoding an input signal whichwas already encoded, the data in memory 214 can be used to discern whichof the encoding processes was performed first. That is, from the inputsignal data in memory 214 (albeit incomplete), it is generally possibleto determine with which of two code words it has been encoded.

[0171] Yet another alternative for memory 214 is that is can be omittedaltogether.

[0172] One way this can be achieved is to use a deterministic noisesource in the encoding process, such as an algorithmic noise generatorseeded with a known key number. The same deterministic noise source,seeded with the same key number, can be used in the decoding process. Insuch an arrangement, only the key number needs be stored for later usein decoding, instead of the large data set usually stored in memory 214.

[0173] Alternatively, if the noise signal added during encoding does nothave a zero mean value, and the length N of the code word is known tothe decoder, then a universal decoding process can be implemented. Thisprocess uses the same entropy test as the foregoing procedures, butcycles through possible code words, adding/subtracting a small dummynoise value (e.g. less than the expected mean noise value) to every Nthsample of the suspect signal, in accordance with the bits of the codeword being tested, until a reduction in entropy is noted. Such anapproach is not favored for most applications, however, because itoffers less security than the other embodiments (e.g. it is subject tocracking by brute force).

[0174] Many applications are well served by the embodiment illustratedin FIG. 7, in which different code words are used to produce severaldifferently encoded versions of an input signal, each making use of thesame noise data. More particularly, the embodiment 240 of FIG. 7includes a noise store 242 into which noise from source 206 is writtenduring the identification-coding of the input signal with a first codeword. (The noise source of FIG. 7 is shown outside of the real timeencoder 202 for convenience of illustration.) Thereafter, additionalidentification-coded versions of the input signal can be produced byreading the stored noise data from the store and using it in conjunctionwith second through Nth code words to encode the signal. (Whilebinary-sequential code words are illustrated in FIG. 7, in otherembodiments arbitrary sequences of code words can be employed.) Withsuch an arrangement, a great number of differently-encoded signals canbe produced, without requiring a proportionally-sized long term noisememory. Instead, a fixed amount of noise data is stored, whetherencoding an original once or a thousand times.

[0175] (If desired, several differently-coded output signals can beproduced at the same time, rather than seriatim. One such implementationincludes a plurality of adder/subtracter circuits 212, each driven withthe same input signal and with the same scaled noise signal, but withdifferent code words. Each, then, produces a differently encoded outputsignal.)

[0176] In applications having a great number of differently-encodedversions of the same original, it will be recognized that the decodingprocess need not always discern every bit of the code word. Sometimes,for example, the application may require identifying only a group ofcodes to which the suspect signal belongs. (E.g., high order bits of thecode word might indicate an organization to which several differentlycoded versions of the same source material were provided, with low-orderbits identifying specific copies. To identify the organization withwhich a suspect signal is associated, it may not be necessary to examinethe low order bits, since the organization can be identified by the highorder bits alone.) If the identification requirements can be met bydiscerning a subset of the code word bits in the suspect signal, thedecoding process can be shortened.

[0177] Some applications may be best served by restarting the encodingprocess—sometimes with a different code word—several times within anintegral work. Consider, as an example, videotaped productions (e.g.television programming). Each frame of a videotaped production can beidentification-coded with a unique code number, processed in real-timewith an arrangement 248 like that shown in FIG. 8. Each time a verticalretrace is detected by sync detector 250, the noise source 206 resets(e.g. to repeat the sequence just produced) and an identification codeincrements to the next value. Each frame of the videotape is therebyuniquely identification-coded. Typically, the encoded signal is storedon a videotape for long term storage (although other storage media,including laser disks, can be used).

[0178] Returning to the encoding apparatus, the look-up table 204 in theillustrated embodiment exploits the fact that high amplitude samples ofthe input data signal can tolerate (without objectionable degradation ofthe output signal) a higher level of encoded identification coding thancan low amplitude input samples. Thus, for example, input data sampleshaving decimal values of 0, 1 or 2 may be correspond (in the look-uptable 204) to scale factors of unity (or even zero), whereas input datasamples having values in excess of 200 may correspond to scale factorsof 15. Generally speaking, the scale factors and the input sample valuescorrespond by a square root relation. That is, a four-fold increase in avalue of the sampled input signal corresponds to approximately atwo-fold increase in a value of the scaling factor associated therewith.

[0179] (The parenthetical reference to zero as a scaling factor alludesto cases, e.g., in which the source signal is temporally or spatiallydevoid of information content. In an image, for example, a regioncharacterized by several contiguous sample values of zero may correspondto a jet black region of the frame. A scaling value of zero may beappropriate here since there is essentially no image data to bepirated.)

[0180] Continuing with the encoding process, those skilled in the artwill recognized the potential for “rail errors” in the illustratedembodiment. For example, if the input signal consists of 8-bit samples,and the samples span the entire range from 0 to 255 (decimal), then theaddition or subtraction of scaled noise to/from the input signal mayproduce output signals that cannot be represented by 8 bits (e.g. −2, or257). A number of well-understood techniques exist to rectify thissituation, some of them proactive and some of them reactive. (Amongthese known techniques are: specifying that the input signal shall nothave samples in the range of 04 or 251-255, thereby safely permittingmodulation by the noise signal; or including provision for detecting andadaptively modifying input signal samples that would otherwise causerail errors.)

[0181] While the illustrated embodiment describes stepping through thecode word sequentially, one bit at a time, to control modulation ofsuccessive bits of the input signal, it will be appreciated that thebits of the code word can be used other than sequentially for thispurpose. Indeed, bits of the code word can be selected in accordancewith any predetermined algorithm.

[0182] The dynamic scaling of the noise signal based on theinstantaneous value of the input signal is an optimization that can beomitted in many embodiments. That is, the look-up table 204 and thefirst scaler 208 can be omitted entirely, and the signal from thedigital noise source 206 applied directly (or through the second, globalscaler 210) to the adder/subtracter 212.

[0183] It will be further recognized that the use of a zero-mean noisesource simplifies the illustrated embodiment, but is not necessary tothe invention. A noise signal with another mean value can readily beused, and D.C. compensation (if needed) can be effected elsewhere in thesystem.

[0184] The use of a noise source 206 is also optional. A variety ofother signal sources can be used, depending on application-dependentconstraints (e.g. the threshold at which the encoded identificationsignal becomes perceptible). In many instances, the level of theembedded identification signal is low enough that the identificationsignal needn't have a random aspect; it is imperceptible regardless ofits nature. A pseudo random source 206, however, is usually desiredbecause it provides the greatest identification code signal S/N ratio (asomewhat awkward term in this instance) for a level of imperceptibilityof the embedded identification signal.

[0185] It will be recognized that identification coding need not occurafter a signal has been reduced to stored form as data (i.e. “fixed intangible form,” in the words of the U.S. Copyright Act). Consider, forexample, the case of popular musicians whose performance are oftenrecorded illicitly. By identification coding the audio before it drivesconcert hall speakers, unauthorized recordings of the concert can betraced to a particular place and time. Likewise, live audio sources suchas 911 emergency calls can be encoded prior to recording so as tofacilitate their later authentication.

[0186] While the black box embodiment has been described as a standalone unit, it will be recognized that it can be integrated into anumber of different tools/instruments as a component. One is a scanner,which can embed identification codes in the scanned output data. (Thecodes can simply serve to memorialize that the data was generated by aparticular scanner). Another is in creativity software, such as populardrawing/graphics/animation/paint programs offered by Adobe, Macromedia,Corel, and the like.

[0187] Finally, while the real-time encoder 202 has been illustratedwith reference to a particular hardware implementation, it will berecognized that a variety of other implementations can alternatively beemployed. Some utilize other hardware configurations. Others make use ofsoftware routines for some or all of the illustrated functional blocks.(The software routines can be executed on any number of differentgeneral purpose programmable computers, such as 80×86 PC-compatiblecomputers, RISC-based workstations, etc.)

[0188] Types of Noise, Quasi-Noise and Optimized-Noise

[0189] Heretofore this disclosure postulated Gaussian noise, “whitenoise,” and noise generated directly from application instrumentation asa few of the many examples of the kind of carrier signal appropriate tocarry a single bit of information throughout an image or signal. It ispossible to be even more proactive in “designing” characteristics ofnoise in order to achieve certain goals. The . “design” of usingGaussian or instrumental noise was aimed somewhat toward “absolute”security. This section of the disclosure takes a look at otherconsiderations for the design of the noise signals which may beconsidered the ultimate carriers of the identification information.

[0190] For some applications it might be advantageous to design thenoise carrier signal (e.g. the Nth embedded code signal in the firstembodiment; the scaled noise data in the second embodiment), so as toprovide more absolute signal strength to the identification signalrelative to the perceptibility of that signal. One example is thefollowing. It is recognized that a true Gaussian noise signal has thevalue ‘0’ occur most frequently, followed by 1 and −1 at equalprobabilities to each other but lower than ‘0’, 2 and −2 next, and soon. Clearly, the value zero carries no information as it is used in theservice of this invention. Thus, one simple adjustment, or design, wouldbe that any time a zero occurs in the generation of the embedded codesignal, a new process takes over, whereby the value is converted“randomly” to either a 1 or a −1. In logical terms, a decision would bemade: if ‘0’, then random(1,−1). The histogram of such a process wouldappear as a Gaussian/Poissonian type distribution, except that the 0 binwould be empty and the 1 and −1 bin would be increased by half the usualhistogram value of the 0 bin.

[0191] In this case, identification signal energy would always beapplied at all parts of the signal. A few of the trade-offs include:there is a (probably negligible) lowering of security of the codes inthat a “deterministic component” is a part of generating the noisesignal. The reason this might be completely negligible is that we stillwind up with a coin flip type situation on randomly choosing the 1 orthe −1. Another trade-off is that this type of designed noise will havea higher threshold of perceptibility, and will only be applicable toapplications where the least significant bit of a data stream or imageis already negligible relative to the commercial value of the material,i.e. if the least significant bit were stripped from the signal (for allsignal samples), no one would know the difference and the value of thematerial would not suffer. This blocking of the zero value in theexample above is but one of many ways to “optimize” the noise propertiesof the signal carrier, as anyone in the art can realize. We refer tothis also as “quasi-noise” in the sense that natural noise can betransformed in a pre-determined way into signals which for all intentsand purposes will read as noise. Also, cryptographic methods andalgorithms can easily, and often by definition, create signals which areperceived as completely random. Thus the word “noise” can have differentconnotations, primarily between that as defined subjectively by anobserver or listener, and that defined mathematically. The difference ofthe latter is that mathematical noise has different properties ofsecurity and the simplicity with which it can either be “sleuthed” orthe simplicity with which instruments can “automatically recognize” theexistence of this noise.

[0192] “Universal” Embedded Codes

[0193] The bulk of this disclosure teaches that for absolute security,the noise-like embedded code signals which carry the bits of informationof the identification signal should be unique to each and every encodedsignal, or, slightly less restrictive, that embedded code signals shouldbe generated sparingly, such as using the same embedded codes for abatch of 1000 pieces of film, for example. Be this as it may, there is awhole other approach to this issue wherein the use of what we will call“universal” embedded code signals can open up large new applications forthis technology. The economics of these uses would be such that the defacto lowered security of these universal codes (e.g. they would beanalyzable by time honored cryptographic decoding methods, and thuspotentially thwarted or reversed) would be economically negligiblerelative to the economic gains that the intended uses would provide.Piracy and illegitimate uses would become merely a predictable “cost”and a source of uncollected revenue only; a simple line item in aneconomic analysis of the whole. A good analogy of this is in the cableindustry and the scrambling of video signals. Everybody seems to knowthat crafty, skilled technical individuals, who may be generally lawabiding citizens, can climb a ladder and flip a few wires in their cablejunction box in order to get all the pay channels for free. The cableindustry knows this and takes active measures to stop it and prosecutethose caught, but the “lost revenue” derived from this practice remainsprevalent but almost negligible as a percentage of profits gained fromthe scrambling system as a whole. The scrambling system as a whole is aneconomic success despite its lack of “absolute security.”

[0194] The same holds true for applications of this technology wherein,for the price of lowering security by some amount, large economicopportunity presents itself. This section first describes what is meantby universal codes, then moves on to some of the interesting uses towhich these codes can be applied.

[0195] Universal embedded codes generally refer to the idea thatknowledge of the exact codes can be distributed. The embedded codeswon't be put into a dark safe never to be touched until litigationarises (as alluded to in other parts of this disclosure), but insteadwill be distributed to various locations where on-the-spot analysis cantake place. Generally this distribution will still take place within asecurity controlled environment, meaning that steps will be taken tolimit the knowledge of the codes to those with a need to know.Instrumentation which attempts to automatically detect copyrightedmaterial is a non-human example of “something” with a need to know thecodes.

[0196] There are many ways to implement the idea of universal codes,each with their own merits regarding any given application. For thepurposes of teaching this art, we separate these approaches into threebroad categories: universal codes based on libraries, universal codesbased on deterministic formula, and universal codes based on pre-definedindustry standard patterns. A rough rule of thumb is that the first ismore secure than the latter two, but that the latter two are possiblymore economical to implement than the first.

[0197] Universal Codes: 1) Libraries of Universal Codes

[0198] The use of libraries of universal codes simply means that thetechniques of this invention are employed as described, except for thefact that only a limited set of the individual embedded code signals aregenerated and that any given encoded material will make use of somesub-set of this limited “universal set.” An example is in order here. Aphotographic print paper manufacturer may wish to pre-expose every pieceof 8 by 10 inch print paper which they sell with a unique identificationcode. They also wish to sell identification code recognition software totheir large customers, service bureaus, stock agencies, and individualphotographers, so that all these people can not only verify that theirown material is correctly marked, but so that they can also determine ifthird party material which they are about to acquire has been identifiedby this technology as being copyrighted. This latter information willhelp them verify copyright holders and avoid litigation, among manyother benefits. In order to “economically” institute this plan, theyrealize that generating unique individual embedded codes for each andevery piece of print paper would generate Terabytes of independentinformation, which would need storing and to which recognition softwarewould need access. Instead, they decide to embed their print paper with16 bit identification codes derived from a set of only 50 independent“universal” embedded code signals. The details of how this is done arein the next paragraph, but the point is that now their recognitionsoftware only needs to contain a limited set of embedded codes in theirlibrary of codes, typically on the order of 1 Megabyte to 10 Megabytesof information for 50×16 individual embedded codes splayed out onto an8×10 photographic print (allowing for digital compression). The reasonfor picking 50 instead of just 16 is one of a little more addedsecurity, where if it were the same 16 embedded codes for allphotographic sheets, not only would the serial number capability belimited to 2 to the 16th power, but lesser and lesser sophisticatedpirates could crack the codes and remove them using software tools.

[0199] There are many different ways to implement this scheme, where thefollowing is but one exemplary method. It is determined by the wisdom ofcompany management that a 300 pixels per inch criteria for the embeddedcode signals is sufficient resolution for most applications. This meansthat a composite embedded code image will contain 3000 pixels by 2400pixels to be exposed at a very low level onto each 8×10 sheet. Thisgives 7.2 million pixels. Using our staggered coding system such asdescribed in the black box implementation of FIGS. 5 and 6, eachindividual embedded code signal will contain only 7.2 million divided by16, or approximately 450K true information carrying pixels, i.e. every16th pixel along a given raster line. These values will typically be inthe range of 2 to −2 in digital numbers, or adequately described by asigned 3 bit number. The raw information content of an embedded code isthen approximately ⅜th's bytes times 450K or about 170 Kilobytes.Digital compression can reduce this further. All of these decisions aresubject to standard engineering optimization principles as defined byany given application at hand, as is well known in the art. Thus we findthat 50 of these independent embedded codes will amount to a fewMegabytes. This is quite reasonable level to distribute as a “library”of universal codes within the recognition software. Advanced standardencryption devices could be employed to mask the exact nature of thesecodes if one were concerned that would-be pirates would buy therecognition software merely to reverse engineer the universal embeddedcodes. The recognition software could simply unencrypt the codes priorto applying the recognition techniques taught in this disclosure.

[0200] The recognition software itself would certainly have a variety offeatures, but the core task it would perform is determining if there issome universal copyright code within a given image. The key questionsbecome WHICH 16 of the total 50 universal codes it might contain, ifany, and if there are 16 found, what are their bit values. The keyvariables in determining the answers to these questions are:registration, rotation, magnification (scale), and extent. In the mostgeneral case with no helpful hints whatsoever, all variables must beindependently varied across all mutual combinations, and each of the 50universal codes must then be checked by adding and subtracting to see ifan entropy decrease occurs. Strictly speaking, this is an enormous job,but many helpful hints will be found which make the job much simpler,such as having an original image to compare to the suspected copy, orknowing the general orientation and extent of the image relative to an8×10 print paper, which then through simple registration techniques candetermine all of the variables to some acceptable degree. Then it merelyrequires cycling through the 50 universal codes to find any decrease inentropy. If one does, then 15 others should as well. A protocol needs tobe set up whereby a given order of the 50 translates into a sequence ofmost significant bit through least significant bit of the ID code word.Thus if we find that universal code number “4” is present, and we findits bit value to be “0”, and that universal codes “1” through “3?” aredefinitely not present, then our most significant bit of our N-bit IDcode number is a “0”. Likewise, we find that the next lowest universalcode present is number “7” and it turns out to be a “1”, then our nextmost significant bit is a “1”. Done properly, this system can cleanlytrace back to the copyright owner so long as they registered theirphotographic paper stock serial number with some registry or with themanufacturer of the paper itself. That is, we look up in the registrythat a paper using universal embedded codes 4, 7, 11, 12, 15, 19, 21,26, 27, 28, 34, 35, 37, 38, 40, and 48, and having the embedded code0110 0101 0111 0100 belongs to Leonardo de Boticelli, an unknownwildlife photographer and glacier cinematographer whose address is inNorthern Canada. We know this because he dutifully registered his filmand paper stock, a few minutes of work when he bought the stock, whichhe plopped into the “no postage necessary” envelope that themanufacturing company kindly provided to make the process ridiculouslysimple. Somebody owes Leonardo a royalty check it would appear, andcertainly the registry has automated this royalty payment process aspart of its services.

[0201] One final point is that truly sophisticated pirates and otherswith illicit intentions can indeed employ a variety of cryptographic andnot so cryptographic methods to crack these universal codes, sell them,and make software and hardware tools which can assist in the removing ordistorting of codes. We shall not teach these methods as part of thisdisclosure, however. In any event, this is one of the prices which mustbe paid for the ease of universal codes and the applications they openup.

[0202] Universal Codes: 2) Universal Codes Based on DeterministicFormulas

[0203] The libraries of universal codes require the storage andtransmittal of Megabytes of independent, generally random data as thekeys with which to unlock the existence and identity of signals andimagery that have been marked with universal codes. Alternatively,various deterministic formulas can be used which “generate” what appearto be random data/image frames, thereby obviating the need to store allof these codes in memory and interrogate each and of the “50” universalcodes. Deterministic formulas can also assist in speeding up the processof determining the ID code once one is known to exist in a given signalor image. On the other hand, deterministic formulas lend themselves tosleuthing by less sophisticated pirates. And once sleuthed, they lendthemselves to easier communication, such as posting on the Internet to ahundred newsgroups. There may well be many applications which do notcare about sleuthing and publishing, and deterministic formulas forgenerating the individual universal embedded codes might be just theticket.

[0204] Universal Codes: 3) “Simple” Universal Codes

[0205] This category is a bit of a hybrid of the first two, and is mostdirected at truly large scale implementations of the principles of thistechnology. The applications employing this class are of the type wherestaunch security is much less important than low cost, large scaleimplementation and the vastly larger economic benefits that thisenables. One exemplary application is placement of identificationrecognition units directly within modestly priced home audio and videoinstrumentation (such as a TV). Such recognition units would typicallymonitor audio and/or video looking for these copyright identificationcodes, and thence triggering simple decisions based on the findings,such as disabling or enabling recording capabilities, or incrementingprogram specific billing meters which are transmitted back to a centralaudio/video service provider and placed onto monthly invoices. Likewise,it can be foreseen that “black boxes” in bars and other public placescan monitor (listen with a microphone) for copyrighted materials andgenerate detailed reports, for use by ASCAP, BMI, and the like.

[0206] A core principle of simple universal codes is that some basicindustry standard “noiselike” and seamlessly repetitive patterns areinjected into signals, images, and image sequences so that inexpensiverecognition units can either A) determine the mere existence of acopyright “flag”, and B) additionally to A, determine preciseidentification information which can facilitate more complex decisionmaking and actions.

[0207] In order to implement this particular embodiment of the presentinvention, the basic principles of generating the individual embeddednoise signals need to be simplified in order to accommodate inexpensiverecognition signal processing circuitry, while maintaining theproperties of effective randomness and holographic permeation. Withlarge scale industry adoption of these simple codes, the codesthemselves would border on public domain information (much as cablescrambling boxes are almost de facto public domain), leaving the dooropen for determined pirates to develop black market countermeasures, butthis situation would be quite analogous to the scrambling of cable videoand the objective economic analysis of such illegal activity.

[0208] One prior art known to the applicant in this general area ofpro-active copyright detection is the Serial Copy Management Systemadopted by many firms in the audio industry. To the best of applicant'sknowledge, this system employs a non-audio “flag” signal which is notpart of the audio data stream, but which is nevertheless grafted ontothe audio stream and can indicate whether the associated audio datashould or should not be duplicated. One problem with this system is thatit is restricted to media and instrumentation which can support thisextra “flag” signal. Another deficiency is that the flagging systemcarries no identity information which would be useful in making morecomplex decisions. Yet another difficulty is that high quality audiosampling of an analog signal can come arbitrarily close to making aperfect digital copy of some digital master and there seems to be noprovision for inhibiting this possibility.

[0209] The principles of this invention can be brought to bear on theseand other problems, in audio applications, video, and all of the otherapplications previously discussed. An exemplary application of simpleuniversal codes is the following. A single industry standard “1.000000second of noise” would be defined as the most basic indicator of thepresence or absence of the copyright marking of any given audio signal.FIG. 9 has an example of what the waveform of an industry standard noisesecond might look like, both in the time domain 400 and the frequencydomain 402. It is by definition a continuous function and would adapt toany combination of sampling rates and bit quanitizations. It has anormalized amplitude and can be scaled arbitrarily to any digital signalamplitude. The signal level and the first M'th derivatives of the signalare continuous at the two boundaries 404 (FIG. 9C), such that when it isrepeated, the “break” in the signal would not be visible (as a waveform)or audible when played through a high end audio system. The choice of 1second is arbitrary in this example, where the precise length of theinterval will be derived from considerations such as audibility,quasi-white noise status, seamless repeatability, simplicity ofrecognition processing, and speed with which a copyright markingdetermination can be made. The injection of this repeated noise signalonto a signal or image (again, at levels below human perception) wouldindicate the presence of copyright material. This is essentially a onebit identification code, and the embedding of further identificationinformation will be discussed later on in this section. The use of thisidentification technique can extend far beyond the low cost homeimplementations discussed here, where studios could use the technique,and monitoring stations could be set up which literally monitor hundredsof channels of information simultaneously, searching for marked datastreams, and furthermore searching for the associated identity codeswhich could be tied in with billing networks and royalty trackingsystems.

[0210] This basic, standardized noise signature is seamlessly repeatedover and over again and added to audio signals which are to be markedwith the base copyright identification. Part of the reason for the word“simple” is seen here: clearly pirates will know about this industrystandard signal, but their illicit uses derived from this knowledge,such as erasure or corruption, will be economically minuscule relativeto the economic value of the overall technique to the mass market. Formost high end audio this signal will be some 80 to 100 dB down from fullscale, or even much further; each situation can choose its own levelsthough certainly there will be recommendations. The amplitude of thesignal can be modulated according to the audio signal levels to whichthe noise signature is being applied, i.e. the amplitude can increasesignificantly when a drum beats, but not so dramatically as to becomeaudible or objectionable. These measures merely assist the recognitioncircuitry to be described.

[0211] Recognition of the presence of this noise signature by low costinstrumentation can be effected in a variety of ways. One rests on basicmodifications to the simple principles of audio signal power metering.Software recognition programs can also be written, and moresophisticated mathematical detection algorithms can be applied to audioin order to make higher confidence detection identifications. In suchembodiments, detection of the copyright noise signature involvescomparing the time averaged power level of an audio signal with the timeaveraged power level of that same audio signal which has had the noisesignature subtracted from it. If the audio signal with the noisesignature subtracted has a lower power level that the unchanged audiosignal, then the copyright signature is present and some status flag tothat effect needs to be set. The main engineering subtleties involved inmaking this comparison include: dealing with audio speed playbackdiscrepancies (e.g. an instrument might be 0.5% “slow” relative toexactly one second intervals); and, dealing with the unknown phase ofthe one second noise signature within any given audio (basically, its“phase” can be anywhere from 0 to 1 seconds). Another subtlety, not socentral as the above two but which nonetheless should be addressed, isthat the recognition circuits should not subtract a higher amplitude ofthe noise signature than was originally embedded onto the audio signal.Fortunately this can be accomplished by merely subtracting only a smallamplitude of the noise signal, and if the power level goes down, this isan indication of “heading toward a trough” in the power levels. Yetanother related subtlety is that the power level changes will be verysmall relative to the overall power levels, and calculations generallywill need to be done with appropriate bit precision, e.g. 32 bit valueoperations and accumulations on 16-20 bit audio in the calculations oftime averaged power levels.

[0212] Clearly, designing and packaging this power level comparisonprocessing circuitry for low cost applications is an engineeringoptimization task. One trade-off will be the accuracy of making anidentification relative to the “short-cuts” which can be made to thecircuitry in order to lower its cost and complexity. A preferredembodiment for the placement of this recognition circuitry inside ofinstrumentation is through a single programmable integrated circuitwhich is custom made for the task. FIG. 10 shows one such integratedcircuit 506. Here the audio signal comes in, 500, either as a digitalsignal or as an analog signal to be digitized inside the IC 500, and theoutput is a flag 502 which is set to one level if the copyright noisesignature is found, and to another level if it is not found. Alsodepicted is the fact that the standardized noise signature waveform isstored in Read Only Memory, 504, inside the IC 506. There will be aslight time delay between the application of an audio signal to the IC506 and the output of a valid flag 502, due to the need to monitor somefinite portion of the audio before a recognition can place. In thiscase, there may need to be a “flag valid” output 508 where the ICinforms the external world if it has had enough time to make a properdetermination of the presence or absence of the copyright noisesignature.

[0213] There are a wide variety of specific designs and philosophies ofdesigns applied to accomplishing the basic function of the IC 506 ofFIG. 10. Audio engineers and digital signal processing engineers areable to generate several fundamentally different designs. One suchdesign is depicted in FIG. 11 by a process 599, which itself is subjectto further engineering optimization as will be discussed. FIG. 11depicts a flow chart for any of: an analog signal processing network, adigital signal processing network, or programming steps in a softwareprogram. We find an input signal 600 which along one path is applied toa time averaged power meter 602, and the resulting power output itselftreated as a signal P_(sig). To the upper right we find the standardnoise signature 504 which will be read out at 125% of normal speed, 604,thus changing its pitch, giving the “pitch changed noise signal” 606.Then the input signal has this pitch changed noise signal subtracted instep 608, and this new signal is applied to the same form of timeaveraged power meter as in 602, here labelled 610. The output of thisoperation is also a time based signal here labelled as P_(s-pcn), 610.Step 612 then subtracts the power signal 602 from the power signal 610,giving an output difference signal P_(out), 613. If the universalstandard noise signature does indeed exist on the input audio signal600, then case 2, 616, will be created wherein a beat signal 618 ofapproximately 4 second period will show up on the output signal 613, andit remains to detect this beat signal with a step such as in FIG. 12,622. Case 1, 614, is a steady noisy signal which exhibits no periodicbeating. 125% at step 604 is chosen arbitrarily here, where engineeringconsiderations would determine an optimal value, leading to differentbeat signal frequencies 618. Whereas waiting 4 seconds in this examplewould be quite a while, especially is you would want to detect at leasttwo or three beats, FIG. 12 outlines how the basic design of FIG. 11could be repeated and operated upon various delayed versions of theinput signal, delayed by something like {fraction (1/20)}th of a second,with 20 parallel circuits working in concert each on a segment of theaudio delayed by 0.05 seconds from their neighbors. In this way, a beatsignal will show up approximately every ⅕th of a second and will looklike a travelling wave down the columns of beat detection circuits. Theexistence or absence of this travelling beat wave triggers the detectionflag 502. Meanwhile, there would be an audio signal monitor 624 whichwould ensure that, for example, at least two seconds of audio has beenheard before setting the flag valid signal 508.

[0214] Though the audio example was described above, it should be clearto anyone in the art that the same type of definition of some repetitiveuniversal noise signal or image could be applied to the many othersignals, images, pictures, and physical media already discussed.

[0215] The above case deals only with a single bit plane of information,i.e., the noise signature signal is either there (1) or it isn't (0).For many applications, it would be nice to detect serial numberinformation as well, which could then be used for more complexdecisions, or for logging information on billing statements or whatnot.The same principles as the above would apply, but now there would be Nindependent noise signatures as depicted in FIG. 9 instead one singlesuch signature. Typically, one such signature would be the master uponwhich the mere existence of a copyright marking is detected, and thiswould have generally higher power than the others, and then the otherlower power “identification” noise signatures would be embedded intoaudio. Recognition circuits, once having found the existence of theprimary noise signature, would then step through the other N noisesignatures applying the same steps as described above. Where a beatsignal is detected, this indicates the bit value of ‘1’, and where nobeat signal is detected, this indicates a bit value of ‘0’. It might betypical that N will equal 32, that way 232 number of identificationcodes are available to any given industry employing this invention.

[0216] Use of this Technology When the Length of the Identification Codeis 1

[0217] The principles of this invention can obviously be applied in thecase where only a single presence or absence of an identificationsignal—a fingerprint if you will—is used to provide confidence that somesignal or image is copyrighted. The example above of the industrystandard noise signature is one case in point. We no longer have theadded confidence of the coin flip analogy, we no longer have trackingcode capabilities or basic serial number capabilities, but manyapplications may not require these attributes and the added simplicityof a single fingerprint might outweigh these other attributes in anyevent.

[0218] The “Wallpaper” Analogy

[0219] The term “holographic” has been used in this disclosure todescribe how an identification code number is distributed in a largelyintegral form throughout an encoded signal or image. This also refers tothe idea that any given fragment of the signal or image contains theentire unique identification code number. As with physicalimplementations of holography, there are limitations on how small afragment can become before one begins to lose this property, where theresolution limits of the holographic media are the main factor in thisregard for holography itself. In the case of an uncorrupted distributionsignal which has used the encoding device of FIG. 5, and whichfurthermore has used our “designed noise” of above wherein the zero'swere randomly changed to a 1 or −1, then the extent of the fragmentrequired is merely N contiguous samples in a signal or image rasterline, where N is as defined previously being the length of ouridentification code number. This is an informational extreme; practicalsituations where noise and corruption are operative will requiregenerally one, two or higher orders of magnitude more samples than thissimple number N. Those skilled in the art will recognize that there aremany variables involved in pinning down precise statistics on the sizeof the smallest fragment with which an identification can be made.

[0220] For tutorial purposes, the applicant also uses the analogy thatthe unique identification code number is “wallpapered” across and image(or signal). That is, it is repeated over and over again all throughoutan image. This repetition of the ID code number can be regular, as inthe use of the encoder of FIG. 5, or random itself, where the bits inthe ID code 216 of FIG. 6 are not stepped through in a normal repetitivefashion but rather are randomly selected on each sample, and the randomselection stored along with the value of the output 228 itself. in anyevent, the information carrier of the ID code, the individual embeddedcode signal, does change across the image or signal. Thus as thewallpaper analogy summarizes: the ID code repeats itself over and over,but the patterns that each repetition imprints change randomlyaccordingly to a generally unsleuthable key.

[0221] Towards Steganography Proper and the Use of this Technology inPassing More Complex Messages or Information

[0222] This disclosure concentrates on what above was calledwallpapering a single identification code across an entire signal. Thisappears to be a desirable feature for many applications. However, thereare other applications where it might be desirable to pass messages orto embed very long strings of pertinent identification information insignals and images. One of many such possible applications would bewhere a given signal or image is meant to be manipulated by severaldifferent groups, and that certain regions of an image are reserved foreach group's identification and insertion of pertinent manipulationinformation.

[0223] In these cases, the code word 216 in FIG. 6 can actually changein some pre-defined manner as a function of signal or image position.For example, in an image, the code could change for each and everyraster line of the digital image. It might be a 16 bit code word, 216,but each scan line would have a new code word, and thus a 480 scan lineimage could pass a 980 (480×2 bytes) byte message. A receiver of themessage would need to have access to either the noise signal stored inmemory 214, or would have to know the universal code structure of thenoise codes if that method of coding was being used. To the best ofapplicant's knowledge, this is a novel approach to the mature field ofsteganography.

[0224] In all three of the foregoing applications of universal codes, itwill often be desirable to append a short (perhaps 8- or 16-bit) privatecode, which users would keep in their own secured places, in addition tothe universal code. This affords the user a further modicum of securityagainst potential erasure of the universal codes by sophisticatedpirates.

[0225] Conclusion

[0226] In view of the great number of different embodiments to which theprinciples of my invention can be put, it should be recognized that thedetailed embodiments are illustrative only and should not be taken aslimiting the scope of my invention. Rather, I claim as my invention allsuch embodiments as may come within the scope and spirit of thefollowing claims, and equivalents thereto.

I claim:
 1. A method of encoding a digital image to permit its lateridentification, the image comprising plural pixels, the methodcomprising: receiving an N-bit data string to be embedded in the image,N being at least two, the string comprising bits having first and secondvalues, one of said values being “1” and the other of said values being“0”; if the first bit of the string has the first value, making acorresponding change to the image, said change being essentiallyimperceptible to a human viewer of the image, but if the first bit ofthe string has the second value, making no corresponding change to theimage; performing the aforesaid step for the second through Nth bits ofthe string.
 2. The method of claim 1 that further comprises obtaining asuspect image, and determining whether pixels therein evidence changescorresponding to said N-bit data string, in which case the suspect imagemay be matched to said digital image.
 3. The method of claim 1 in whicheach of said corresponding changes alters the values of plural pixels inthe image.
 4. A method of processing image data to encode an N-bitstring of data therein, comprising: providing a frame of input imagedata; providing N sets of pseudo noise, each set corresponding to arespective bit position in said N-bit string; combining sets of saidpseudo noise in accordance with the N-bits of the string to yield acomposite set of pseudo noise; and combining the composite set of pseudonoise with the frame of input image data to yield an encoded frame ofimage data.
 5. The method of claim 4 in which the composite set ofpseudo noise comprises a frame of data coextensive with said frame ofinput image data, and the method includes scaling elements of said frameof composite pseudo noise in accordance with attributes of the frame ofinput image data.
 6. The method of claim 4 that includes combining thecomposite set of pseudo noise with the frame of input image data byadding.
 7. The method of claim 4 wherein the sets of pseudo noise arecombined to yield the composite set by summing the sets for which thecorresponding bit position has a first binary value.
 8. The method ofclaim 7 wherein the sets of pseudo noise are further combined bysubtracting the sets for which the corresponding bit position has asecond, opposite, binary value.
 9. The method of claim 4 wherein theN-bit string is holographically encoded throughout the encoded imagedata, wherein the entire N-bit string can be detected from subparts ofthe encoded image data.
 10. The method of claim 4 wherein the image datacomprises video image data.
 11. A computer storage medium having storedthereon software instructions for causing a computer programmed therebyto perform the method of claim
 4. 12. A computer storage medium havingstored thereon an encoded image produced by the method of claim
 4. 13. Acomputer storage medium having stored thereon an encoded video producedby the method of claim
 4. 14. A method of processing image data todecode an N-bit string of data hidden therein, comprising: providing aframe of input image data; providing N sets of pseudo noise, each setcorresponding to a respective bit position in said N-bit string;correlating said image data with the first through Nth sets of pseudonoise to determine values of first through Nth bits of said string,respectively.
 15. The method of claim 14 in which the image datacomprises video data.